We evaluate the proposed method on two popular hard UDA benchmarks. One is VisDA-2017 [1]}, which is a Synthetic-to-Real dataset that consists of 152,397 images from the synthetic domain (Sy) and 55,388 images from the real domain (Re) across 12 categories. The second is Office-Home [2]}, which contains 15,500 images from 65 categories in four domains: Artistic (A), Clipart (C), Product (P), and Real-world (R) images. Following the general setup, we adopt accuracy as the performance metric of each task.
